Thanks for replying Gabor. I checked the leaps() function and i think it is intended to find the best combination of predictors in the linear model. Does leaps have a way to combine different factor columns in my data frame as follows :
I have the regression model fixed. The combination of predictor variables used always remains the same. UncDmd ~ M1 + M2 + M3 + M4 + M5 + M6 + M7 + M8 + M9 + M10 + M11 I want to get the coefficients in this linear model when different combinations of factors (select a combination from first four columns of the data frame) and their levels are taken from a data frame(apply lm model for a each combination of levels within the selected factor columns). Thus corresponding to each combination, the data used to determine the model coefficients will be different. I am attaching the data and R files (long method using loops) that I use to get the result. Currently, I modify keys to get different combinations. Also, note in the script, the data frame is named LRO1. Thanks again, Murtaza On Fri, Dec 26, 2008 at 12:58 PM, Gabor Grothendieck <ggrothendi...@gmail.com> wrote: > See the leaps package. > > On Fri, Dec 26, 2008 at 12:37 PM, Murtaza Das <murtaza...@gmail.com> wrote: >> Hi, >> >> I am trying to find an efficient way of applying a linear regression >> model to different factor combinations in a data frame. >> I want to obtain the output with minimal or no use of loops if >> possible. Please let me know if this query is unclear. >> >> Thanks, >> Murtaza >> >> *********************************************************************************************************************************************************** >> >> The data frame TEST1 has four factor columns followed by thirteen >> numeric columns defined as : >> 1) Community, levels: "20232" >> 2) WT, levels: "B", "E", "M" >> 3) LTC, levels: "L", "M", "S", "1" >> 4) UC, levels: "1X1", "2X2" >> 5) UncDmd: Response variable in the linear model >> 6-16) M1...M11: Explanatory variables in the linear model >> >> A few sample rows in the data frame are as follows: >>> TEST1[1:15,] >> Community WT LTC UC UncDmd M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 >> 1 20232 E L 1X1 1.000000 0 0 0 0 0 0 0 0 0 0 1 >> 2 20232 E L 2X2 0.000000 0 0 0 0 0 0 0 0 0 0 1 >> 3 20232 E M 1X1 1.000000 0 0 0 0 0 0 0 0 0 0 1 >> 4 20232 E M 2X2 1.000000 0 0 0 0 0 0 0 0 0 0 1 >> 5 20232 E S 1X1 0.000000 0 0 0 0 0 0 0 0 0 0 1 >> 6 20232 E S 2X2 0.000000 0 0 0 0 1 0 0 0 0 0 0 >> 7 20232 B 1 1X1 0.209117 0 0 0 0 0 0 0 0 0 0 1 >> 8 20232 B 1 2X2 0.190605 0 0 0 0 0 0 0 0 0 0 1 >> 9 20232 B L 1X1 0.000000 0 0 0 0 1 0 0 0 0 0 0 >> 10 20232 B L 2X2 1.000000 0 0 0 0 0 0 0 0 0 0 1 >> 11 20232 B M 1X1 4.000000 0 0 0 0 0 0 0 0 0 0 1 >> 12 20232 B M 2X2 0.000000 0 0 0 0 0 0 0 0 0 0 1 >> 13 20232 B S 1X1 0.000000 1 0 0 0 0 0 0 0 0 0 0 >> 14 20232 B S 2X2 0.000000 0 0 0 0 0 0 0 0 0 0 1 >> 15 20232 M 1 1X1 0.618689 0 0 0 0 0 0 0 0 0 1 0 >> >> ********************************************************************************************************************************************************* >> I need to store the coefficients using lm() for different combinations >> of the 4 factors, or different combinations of 3 factors or different >> combinations of 2 factors or >> differennt combinations of 1 factor. >> The formula remains fixed as: >>> Formula >> UncDmd ~ M1 + M2 + M3 + M4 + M5 + M6 + M7 + M8 + M9 + M10 + M11 >> >> So, different models I want to solve in R are : >> 1) Community : lm(Formula,TEST1[ as.logical( >> (TEST1[[1]]=="20232") ) , ]) >> 2) WT : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="B") ) , ]) >> 3) WT : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="E") ) , ]) >> 4) WT : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="M") ) , ]) >> 5) LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[3]]=="L") ) , ]) >> 6) LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[3]]=="M") ) , ]) >> 7) LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[3]]=="S") ) , ]) >> 8) LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[3]]=="1L") ) , ]) >> 9) UC : lm(Formula,TEST1[ as.logical( >> (TEST1[[4]]=="1X1") ) , ]) >> 10) UC : lm(Formula,TEST1[ as.logical( >> (TEST1[[4]]=="2X2") ) , ]) >> 11) Community, WT : lm(Formula,TEST1[ as.logical( >> (TEST1[[1]]=="20232") * (TEST1[[2]]=="B") ) , ]) >> 12) Community, WT : lm(Formula,TEST1[ as.logical( >> (TEST1[[1]]=="20232") * (TEST1[[2]]=="E") ) , ]) >> 13) Community, WT : lm(Formula,TEST1[ as.logical( >> (TEST1[[1]]=="20232") * (TEST1[[2]]=="M") ) , ]) >> 14) Community, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[1]]=="20232") * (TEST1[[3]]=="L") ) , ]) >> 15) Community, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[1]]=="20232") * (TEST1[[3]]=="M") ) , ]) >> 16) Community, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[1]]=="20232") * (TEST1[[3]]=="S") ) , ]) >> 17) Community, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[1]]=="20232") * (TEST1[[3]]=="1") ) , ]) >> 18) Community, UC : lm(Formula,TEST1[ as.logical( >> (TEST1[[1]]=="20232") * (TEST1[[4]]=="1X1") ) , ]) >> 19) Community, UC : lm(Formula,TEST1[ as.logical( >> (TEST1[[1]]=="20232") * (TEST1[[4]]=="2X2") ) , ]) >> 20) WT, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="B") * (TEST1[[3]]=="L") ) , ]) >> 21) WT, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="B") * (TEST1[[3]]=="M") ) , ]) >> 22) WT, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="B") * (TEST1[[3]]=="S") ) , ]) >> 23) WT, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="B") * (TEST1[[3]]=="1") ) , ]) >> 24) WT, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="E") * (TEST1[[3]]=="L") ) , ]) >> 25) WT, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="E") * (TEST1[[3]]=="M") ) , ]) >> 26) WT, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="E") * (TEST1[[3]]=="S") ) , ]) >> 27) WT, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="E") * (TEST1[[3]]=="1") ) , ]) >> 28) WT, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="M") * (TEST1[[3]]=="L") ) , ]) >> 29) WT, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="M") * (TEST1[[3]]=="M") ) , ]) >> 30) WT, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="M") * (TEST1[[3]]=="S") ) , ]) >> 31) WT, LTC : lm(Formula,TEST1[ as.logical( >> (TEST1[[2]]=="M") * (TEST1[[3]]=="1") ) , ]) >> 32) WT, UC : >> ... >> ... >> xx) LTC, UC : >> ... >> xxx) Community, WT, LTC : >> ... >> ... >> and so on upto: >> xxxx) Community, WT, LTC, UC : lm(Formula,TEST1[ as.logical( >> (TEST1[[1]]=="20232") * (TEST1[[2]]=="M") * (TEST1[[3]]=="1") ) * >> (TEST1[[4]]=="2X2"), ]) >> *********************************************************************************************************************************************************** >> Desired Output format (or something simlar): >> Factor1 Factor2 Factor3 Factor4 Intercept M1 M2 M3 M4 M5 M6 >> M7 M8 M9 M10 M11 >> 1) 20232 x x x >> x x x x x x x x x >> 2) B x x x >> x x x x x x x x x >> 3) E x x x >> x x x x x x x x x >> 4) M x x x >> x x x x x x x x x >> 5) L x x x >> x x x x x x x x x >> 6) M x x x >> x x x x x x x x x >> 7) S x x x >> x x x x x x x x x >> 8) 1 x x x >> x x x x x x x x x >> 9) 1X1 x x x >> x x x x x x x x x >> 10) 2X2 x x x >> x x x x x x x x x >> 11) 20232 B x x x x >> x x x x x x x x >> .. >> .. >> and so on.. >> >> >> x is the respective coefficient obtained from the linear fit. >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> >
______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
